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- 1,373
- Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge925
Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, it is shown that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained.
- Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment390
A deep learning approach based on convolutional neural networks, designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data, outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity.
- Multi-modal classification of Alzheimer's disease using nonlinear graph fusion261
A multi-modality classification framework to efficiently exploit the complementarity in theMulti-modal data is presented and pairwise similarity is calculated for each modality individually using the features including regional MRI volumes, voxel-based FDG-PET signal intensities, CSF biomarker measures, and categorical genetic information.
- Multiple instance learning for classification of dementia in brain MRI214
The proposed multiple instance learning (MIL) method for Alzheimer's disease and its prodromal stage mild cognitive impairment can achieve similar or improved results, providing an alternative framework for the detection and prediction of neurodegenerative diseases.
- A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease162
The proposed biomarker benefits from the contributions of different factors: a tradeoff registration level to align images to the template space, the removal of the normal aging effect, selection of discriminative voxels, and the integration of sparse representation technique and the combination of cognitive measures.
- PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report161
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones and proposes solutions that significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.
- Discriminative dictionary learning for abdominal multi-organ segmentation154
An automated segmentation method is presented for multi-organ segmentation in abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively.
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- Bidirectional Mapping of Brain MRI and PET With 3D Reversible GAN for the Diagnosis of Alzheimer’s Disease124
The experimental results show that the structural and functional information of brain tissue can be mapped well and that the image synthesized by the proposed method is close to the real image, demonstrating the effectiveness of the proposed framework.
- Image Super-Resolution Using Knowledge Distillation95
The proposed method significantly improves the SR performance of a student network without changing its structure, which means that the computational time and the memory consumption do not increase during the testing stage while theSR performance is significantly improved.
- Multiclass diagnosis of stages of Alzheimer's disease using linear discriminant analysis scoring for multimodal data92
The results showed that the proposed framework for AD multiclass diagnosis with a linear discriminant analysis (LDA) scoring method achieved a better performance than the method that did not score multimodal data and the methods in previous studies, thereby indicating that the LDA scoring strategy is an efficient way for multimodalities fusion inAD multiclass classification.
- NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results85
This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.
- Predicting Alzheimer’s Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data78
An extreme learning machine (ELM)-based grading method is developed to efficiently fuse multimodal data and predict MCI-to-AD conversion, resulting in an accurate prediction of MCI-to-AD conversion.
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- O-Net: A Novel Framework With Deep Fusion of CNN and Transformer for Simultaneous Segmentation and Classification55
The experimental results show that the proposed O-Net achieves superior segmentation performance than state-of-the-art approaches, and the segmentation results are beneficial for improving the accuracy of the classification task.
- CoTrFuse: a novel framework by fusing CNN and transformer for medical image segmentation45
The proposed CoTrFuse network is a network that combines the strengths of both CNN and Transformer that outperforms several state-of-the-art segmentation methods, indicating its superiority in medical image segmentation.
- Graph Reasoning-Based Emotion Recognition Network41
This paper proposes a novel framework based on the graph convolutional network for emotion recognition by utilizing the semantic relationships of different regions to achieve superior performance over state-of-the-art approaches for the emotion recognition on the CEAR and AFEW datasets.
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- A Super-resolution Guided Network for Improving Automated Thyroid Nodule Segmentation36
A novel framework to improve the accuracy of thyroid nodules segmentation and provide a new way of ultrasound image segmentation that outperforms most state-of-the-art methods and can provide doctors with reliable auxiliary diagnosis information in clinical practice.
- A Deep Learning Quantification Algorithm for HER2 Scoring of Gastric Cancer24
This is the first study to provide a deep learning quantification algorithm for HER2 scoring of gastric cancer to assist the pathologist's diagnosis and the effectiveness of the proposed method is demonstrated.
- Ntire 2025 Image Shadow Removal Challenge Report22
This work examines the findings of the NTIRE 2025 Shadow Removal Challenge, with 17 teams successfully submitting their solutions during the final evaluation phase, and one focusing on reconstruction fidelity and the other on visual perception through a user study.
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- Nonlinear Graph Fusion for Multi-modal Classification of Alzheimer’s Disease22
A nonlinear graph fusion method is proposed to efficiently exploit the complementarity in the multi-modal data for the classification of AD via a nonlinear combination of the graphs in an iterative cross diffusion process.
- Stain Style Transfer Using Transitive Adversarial Networks21
A novel network that is referred to as Transitive Adversarial Networks (TAN) to transfer the color information among slides from different hospitals or centers is proposed, demonstrating the effectiveness of the proposed TAN method in stain style transfer.
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- NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: AI Flash Portrait (Track 3)19
A comprehensive overview of the NTIRE 2026 3rd Restore Any Image Model (RAIM) challenge, with a specific focus on Track 3: AI Flash Portrait, and comprehensively evaluates the proposed algorithms utilizing a hybrid evaluation system that integrates objective quantitative metrics with rigorous subjective assessment protocols.
- Modeling of the bony pelvis from MRI using a multi-atlas AE-SDM for registration and tracking in image-guided robotic prostatectomy15
A robust and fully automated multi-atlas pipeline for bony pelvis segmentation from MRI, using a MRI appearance embedding statistical deformation model (AE-SDM) which incorporates MRI AE-SDMs guidance is presented.
- LightR-YOLOv5: A compact rotating detector for SARS-CoV-2 antigen-detection rapid diagnostic test results14
LightR-YOLOv5, a compact rotating SARS-CoV-2 antigen-detection RDT results detector is proposed and a new data augmentation approach, Single-Copy–Paste, is proposed for increasing data samples for the specific task of RDT result detection while achieving a small improvement in model accuracy.
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- A Multi-Scale Densely Connected Convolutional Neural Network for Automated Thyroid Nodule Classification13
The proposed n-ClsNet achieves an average accuracy (ACC) score of 93.8% in the thyroid nodule classification task, which outperforms several representative state-of-the-art classification methods.
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- Multimodal Cross Global Learnable Attention Network for MR images denoising with arbitrary modal missing10
A controllable Multimodal Cross-Global Learnable Attention Network (MMCGLANet) for MR image denoising with Arbitrary Modal Missing is proposed, and sequence code is employed to label missing modalities, which allows for Arbitrary Modal Missing during model training, validation, and testing.
- UniMRISegNet: Universal 3D Network for Various Organs and Cancers Segmentation on Multi-Sequence MRI10
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- A blind image super-resolution network guided by kernel estimation and structural prior knowledge8
This work proposes a two stage blind super-resolution network that is based on kernel estimation strategy and is capable of integrating structural texture as prior knowledge and outperforms the state-of-the-art methods in terms of fidelity and recovery of clear details.
- Weakly Supervised Classification for Nasopharyngeal Carcinoma With Transformer in Whole Slide Images8
- A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classifications7
A parameterized GAN (ParaGAN) that effectively controls the changes of synthetic samples among domains and highlights the attention regions for downstream classification is proposed that can consistently outperform the existing augmentation methods with explainable classification on two small-scale medical datasets.
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- Unpaired Stain Style Transfer Using Invertible Neural Networks Based on Channel Attention and Long-Range Residual7
An unsupervised color normalization method based on channel attention and long-range residual, using a technology called invertible neural networks (INN) to transfer the stain style while preserving the tissue semantics between different hospitals or centers, resulting in a virtual stained sample in the sense that no actual stains are used.
- Compact StereoNet: Stereo Disparity Estimation via Knowledge Distillation and Compact Feature Extractor7
An efficient convolutional neural architecture for stereo disparity estimation is proposed and a novel adaptive $Smooth_{L1}$ (ASL) Loss for calculating the similarity between the contextual features of the teacher network and those of the student network is presented, resulting in a more robust distillation process.
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- Multi-scale feature learning on pixels and super-pixels for seminal vesicles MRI segmentation7
The results show that the multi-scale approach and the augmentation of the pixel based features with the super-pixel based features enhances the discriminative power of the learnt classifier which leads to a better quality segmentation in some very difficult cases.
- Distance guided generative adversarial network for explainable medical image classifications6
A distance-guided GAN (DisGAN) that controls the variation degrees of generated samples in the hyperplane space that can apply to different classification architectures and has the potential to extend to multi-class classification.
- Comprehensive Mendelian randomization analysis of low-density lipoprotein cholesterol and multiple cancers5
Analyses using both the HMGCR and PCSK9 genes have shown that LDL-C may be a potential risk factor for breast and prostate cancer, while analyses of the HMGCR gene have also suggested that LDL-C may increase the risk of thyroid cancer and decrease the risk of colorectal cancer.
- Toward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image Information5
A novel approach is proposed that integrates a implicit stereo information discriminator and a hybrid degradation model that ensures effective enhancement while preserving disparity consistency, and demonstrates impressive performance on synthetic and real datasets.
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- FZUImageReg: A toolbox for medical image registration and dose fusion in cervical cancer radiotherapy5
A toolbox (FZUImageReg), which is a user-friendly dose fusion system based on hybrid image registration for radiation dose assessment in cervical cancer radiotherapy, and can achieve accurate registration between EBRT and HDR-BT images and provide radiation dose warping and fusion results in terms of high accuracy and efficiency.
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- New support vector machine-based method for microRNA target prediction4
A new miRNA target-prediction method based on the support vector machine (SVM) model that improves the sensitivity and accuracy of miRNA prediction and can be further improved by providing more training examples.
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- E2-RealSR: efficient and effective real-world super-resolution network based on partial degradation modulation3
This work proposes an efficient and effective real-world image super-resolution network (E2-RealSR) based on partial degradation modulation, which is consisted of a small regression and a lightweight super-resolution network.
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- Multi-angle Dual-task Consistency for Semi-Supervised Medical Image Segmentation3
A novel Multi-angle Dual-task Consistency Network (MDC-Net) for semi-supervised medical image segmentation that overcomes the problem of incomplete segmentation caused by noise in pseudo labels, but also enforces geometric constraints and makes the model more robust.
- Extraction of Features from Patch Based Graphs for the Prediction of Disease Progression in AD3
A new approach to extract different types of features by utilizing the patch relationships within and between subjects is proposed, demonstrating that the integration of patch relationship between and within subjects can aid the prediction of MCI to AD conversion.
- Multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images3
This work presents a framework for multi-atlas based segmentation in situations where the authors have a small number of segmented atlas images, but a large database of unlabeled images is also available.
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- VariMix: A variety-guided data mixing framework for explainable medical image classifications2
A variety-guided data mixing framework (VariMix), which exploits an absolute difference map (ADM) to address the label mismatch problems of mixed medical images and demonstrates the superiority of VariMix compared with the existing GAN- and Mixup-based methods on four public datasets using Swin Transformer V2 and ConvNeXt architectures.
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- Multi-stage network for single image Demoireing2
This work proposes a multi-stage and multi-patch network, which can recover non-homogeneous moire images by aggregating the features of different spatial regions of patches in different stages and introduces a novel Atrous Fusion Module in different atrous rates to learn multi-scale information.
- An Improved Vehicle Detection Algorithm Based on Multi-Intermediate State Machine2
Two common vehicle detection algorithms, fixed threshold algorithm and adaptive threshold algorithm, were analyzed in the vehicle detection system with magnetoresistive sensors, simultaneously and an improved multi-intermediate state machine algorithm for vehicle detection was proposed.
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- MSAN-Net: An End-to-End Multi-Scale Attention Network for Universal Industrial Defect Detection1
Extensive experiments showed that MSAN-Net was outperformed existing SOTA models in industrial defect detection tasks, significantly improving detection accuracy for multi-class defects in complex scenarios, reducing reliance on manual inspection, and effectively lowering scrap losses caused by defects, thus providing a reliable solution for intelligent quality inspection in the electronics manufacturing industry.
- Image harmonization and de-harmonization based on singular value decomposition (SVD) in medical domain1
The proposed SVD-based harmonization and de-harmonization algorithms present a robust solution to the challenges of image variability in medical imaging by addressing inconsistencies across different datasets and imaging modalities, while preserving crucial diagnostic information.
- GLNet: low-light image enhancement via grayscale priors1
Two novel and general approaches are presented, which propose a new loss function to constrain the ratio between the corresponding RGB pixel values on the low- light image and the high-light image, and a new framework named GLNet, which uses the dense residual connection block to obtain the deep features of theLow-light images, and design a gray scale channel network branch to guide the texture restoration on the RGB channels by enhancing the grayscale image.
- Enhanced multi-stage network for defocus deblurring using dual-pixel images1
Experimental results show that the proposed enhanced multi-stage network for defocus deblurring using dual-pixel Images can achieve better accuracy than state-of-the-art approaches on the public DPD dataset.
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- BP Neural Network Algorithm for Multi-sensor Trajectory Separation Based on Maximum Membership Degree1
The experimental tests show that the algorithm which is presented in this paper effectively improves the efficiency and accuracy of the trajectory separation, and has a better application value.
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- Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations–
A quantitative statistical framework for FCCM, which comprises an efficient annotation module, an FCCM lesion segmentation module, and an FCCM lesion quantitative statistics module, which provides a quantitative assessment method for disease progression and drug efficacy study, but also considerably reduces the manual measurement and statistical workload of lesions.
- ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolution–
This paper introduces the concept of stereo adapters and spatial adapters which are incorporated into the pre-trained SISR transformer network and proposes a efficient method for transferring a pre-trained single-image super-resolution transformer network to the domain of stereo image super-resolution (SteISR) through a parameter-efficient fine-tuning (PEFT) method.
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- Dual-Encoder Unet for Single Image HDR Reconstruction with Denoising–
This work proposes the notion of extracting image luminance features and texture features separately by utilizing a dual-input channel encoder-decoder structure and utilizes a spatial feature transform module to implement information interaction between both input branches at different feature scales.
- Distance Guided Generative Adversarial Network for Explainable Binary Classifications–
A distance guided GAN (DisGAN) which controls the variation degrees of generated samples in the hyperplane space which can apply to different classification architectures and has potential to extend to multi-class classification.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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